Hong Xie

Cross-sample consistency regularization can eliminate feature fragmentation in Sparse Autoencoders, leading to more reliable and interpretable latent representations.

Forget struggling with non-convex optimization for causal discovery: a new algorithm extracts causal order directly from score functions with simple matrix operations.

Generative models can fail to produce globally consistent counterfactuals when causal graphs have complex topologies, but a novel sheaf-theoretic framework with entropic regularization can overcome these limitations.

Deterministic causal models can't handle extreme counterfactual interventions without ripping apart, unless you use topology-aware methods.